Metabolic risk factors of ovarian cancer: a review

Ovarian cancer and its management through advanced drug delivery system

Ovarian cancer is the deadliest gynecological cancer, often being diagnosed at a late-stage when the disease is already advanced. This makes it known as the ''silent killer'' due to its high mortality rate among women. The ovary itself is composed of three main types of cells epithelial cells, germ cells, and stromal cells. Over 90% of all occurrences of ovarian cancer are epithelial ovarian carcinoma. Ovarian cancer is responsible for 2.5% of cancer in women. The primary signs include stomach bloating, trouble eating or feeling full rapidly, fatigue, and discomfort during intercourse. The management of ovarian cancer is improved by advanced drug delivery systems because they increase therapeutic targeting, reduce side effects, and overcome drug resistance. Accurate distribution to cancer cells is made possible by methods such as ligand-functionalized nanomedicines, dual-targeted nano-drugs, drug conjugates, antibody-drug conjugates and peptide/folate drug conjugates. These technologies enhance individualized and successful ovarian cancer treatment outcomes by maximizing chemotherapy efficacy, extending drug release, and lowering toxicity.

Integrative prognostic modeling of ovarian cancer: incorporating genetic, clinical, and immunological markers

Ovarian cancer has a high mortality rate, primarily due to late diagnosis and complex pathogenesis. This study develops an integrative prognostic model combining genetic, clinical, and immunological data to predict outcomes in ovarian cancer patients. Utilizing data from The Cancer Genome Atlas (TCGA), we identified significant prognostic genes through differential expression and survival analysis, integrating these with clinical features and immune landscape assessments including immune cell infiltration and checkpoint expression. The risk score effectively predicted patient survival, distinguishing between high and low-risk groups with significant outcome differences. High-risk patients demonstrated poor prognosis, greater immune checkpoint expression, and higher tumor mutational burdens (TMB), suggesting potential responsiveness to immunotherapy. The model's predictive capacity was validated across multiple cohorts, showing consistent performance in survival prediction and treatment response. Calibration curves and decision curve analysis confirmed the model's clinical utility. This study highlights the potential of an integrated approach to enhance personalized treatment strategies in ovarian cancer, aiming to improve patient management and outcomes.

Association between diet quality and ovarian cancer risk and survival

BACKGROUND

Research on diet quality and ovarian cancer is limited. We examined the association between diet quality and ovarian cancer risk and survival in a large prospective cohort.

METHODS

We used data from women in the prospective National Institutes of Health-AARP Diet and Health Study enrolled from 1995 to 1996 who were aged 50-71 years at baseline with follow-up through December 31, 2017. Participants completed a 124-item food frequency questionnaire at baseline, and diet quality was assessed via the Healthy Eating Index-2015, the alternate Mediterranean diet score, and the Dietary Approaches to Stop Hypertension score. Primary outcomes were first primary epithelial ovarian cancer diagnosis from cancer registry data and among those diagnosed with ovarian cancer all-cause mortality. We used a semi-Markov multistate model with Cox proportional hazards regression to account for semicompeting events.

RESULTS

Among 150 643 participants with a median follow-up time of 20.5 years, 1107 individuals were diagnosed with a first primary epithelial ovarian cancer. There was no evidence of an association between diet quality and ovarian cancer risk. Among those diagnosed with epithelial ovarian cancer, 893 deaths occurred with a median survival of 2.5 years. Better prediagnosis diet quality, according to the Healthy Eating Index-2015 (quintile 5 vs quintile 1: hazard ratio [HR] = 0.75, 95% confidence interval [CI] = 0.60 to 0.93) and alternate Mediterranean diet score (quintile 5 vs quintile 1: HR = 0.68, 95% CI = 0.53 to 0.87), was associated with lower all-cause mortality. There was no evidence of an association between Dietary Approaches to Stop Hypertension score and all-cause mortality.

CONCLUSIONS

Better prediagnosis diet quality was associated with lower all-cause mortality after ovarian cancer diagnosis but was not associated with ovarian cancer risk.

Unveiling the causal link between metabolic factors and ovarian cancer risk using Mendelian randomization analysis

Background

Metabolic abnormalities are closely tied to the development of ovarian cancer (OC), yet the relationship between anthropometric indicators as risk indicators for metabolic abnormalities and OC lacks consistency.

Method

The Mendelian randomization (MR) approach is a widely used methodology for determining causal relationships. Our study employed summary statistics from the genome-wide association studies (GWAS), and we used inverse variance weighting (IVW) together with MR-Egger and weighted median (WM) supplementary analyses to assess causal relationships between exposure and outcome. Furthermore, additional sensitivity studies, such as leave-one-out analyses and MR-PRESSO were used to assess the stability of the associations.

Result

The IVW findings demonstrated a causal associations between 10 metabolic factors and an increased risk of OC. Including "Basal metabolic rate" (OR= 1.24, P= 6.86×10-4); "Body fat percentage" (OR= 1.22, P= 8.20×10-3); "Hip circumference" (OR= 1.20, P= 5.92×10-4); "Trunk fat mass" (OR= 1.15, P= 1.03×10-2); "Trunk fat percentage" (OR= 1.25, P= 8.55×10-4); "Waist circumference" (OR= 1.23, P= 3.28×10-3); "Weight" (OR= 1.21, P= 9.82×10-4); "Whole body fat mass" (OR= 1.21, P= 4.90×10-4); "Whole body fat-free mass" (OR= 1.19, P= 4.11×10-3) and "Whole body water mass" (OR= 1.21, P= 1.85×10-3).

Conclusion

Several metabolic markers linked to altered fat accumulation and distribution are significantly associated with an increased risk of OC.

Diabetes and ovarian cancer: risk factors, molecular mechanisms and impact on prognosis

BACKGROUND AND AIM

Diabetes mellitus has been linked to a lower rate of cancer survival and an increase in the incidence of most malignancies. Investigations showed that diabetes might affect ovarian cancer (OC) prognosis and survival. Based on the current information, this study intends to review the risk factors, molecular pathways, and impact of diabetes on OC.

METHODS

The data was derived from online databases, including Web of Science, PubMed, and Scopus. The inclusion criteria were original studies, which included the risk factors, molecular mechanisms, and impact of diabetes on OC. The effect of different antidiabetic drugs was also discussed in this manuscript. All of the clinical, in vivo, and in vitro studies were included in the present study.

RESULTS

The diagnosis of diabetes mellitus negatively affects the survival and prognosis in OC cases. The epidemiologic data shows that the risk of OC increases in patients with diabetes mellitus compared to the healthy population. Insulin-like growth factors family was raised in diabetic patients, which target several mechanisms, including targeting oxidative stress, angiogenesis, and tumor markers. Antidiabetic drugs such as metformin, sitagliptin, and rosiglitazone have a promising effect on elongation of survival and enhancement of prognosis in OC patients.

CONCLUSIONS

Diabetes mellitus is a significant risk factor for OC in women, and it negatively impacts survival and prognosis. Molecular mechanisms such as IGF family, oxidative stress, and inflammatory cytokines have been identified to explain this relationship. Antidiabetic drugs like metformin, sitagliptin, and rosiglitazone have shown promise in improving survival and prognosis of OC patients.

The Effectiveness of Metabolic Bariatric Surgery in Preventing Gynecologic Cancer - from Pathophysiology to Clinical Outcomes

Obesity and cancer represent two pandemics of current civilization, the progression of which has followed parallel trajectories. To time, thirteen types of malignancies have been recognized as obesity-related cancers, including breast (in postmenopausal women), endometrial, and ovarian cancer. Pathophysiologic mechanisms that connect the two entities include insulin resistance, adipokine imbalance, increased peripheral aromatization and estrogen levels, tissue hypoxia, and disrupted immunity in the cellular milieu. Beyond the connection of obesity to carcinogenesis at a molecular and cellular level, clinicians should always be cognizant of the fact that obesity might have secondary impacts on the diagnosis and treatment of gynecologic cancer, including limited access to effective screening programs, resistance to chemotherapy and targeted therapies, persisting lymphedema, etc. Metabolic bariatric surgery represents an attractive intervention not only for decreasing the risk of carcinogenesis in high-risk women living with obesity but most importantly as a measure to improve disease-specific and overall survival in patients with diagnosed obesity-related gynecologic malignancies. The present narrative review summarizes current evidence on the underlying pathophysiologic mechanisms, the clinical data, and the potential applications of metabolic bariatric surgery in all types of gynecologic cancer, including breast, endometrial, ovarian, cervical, vulvar, and vaginal.

Diagnostics and treatment of ovarian cancer in the era of precision medicine - opportunities and challenges

Due to predictions of increasing incidences and deaths from ovarian cancer, this neoplasm is a challenge for modern health care. The advent of NGS technology has made it possible to understand the molecular characteristics of many cancers, including ovarian cancer. The data obtained in research became the basis for the development of molecularly targeted therapies thus leading to the entry of NGS analysis into the diagnostic process of oncological patients. This review presents targeted therapies currently in preclinical or clinical trials, whose promising results offer hope for their use in clinical practice in the future. As more therapeutic options emerge, it will be necessary to modify molecular diagnostic regimens to select the best treatment for a given patient. New biomarkers are needed to predict the success of planned therapy. An important aspect of public health is molecular testing in women with a familial predisposition to ovarian cancer enabling patients to be included in prevention programs. NGS technology, despite its high throughput, poses many challenges, from the quality of the diagnostic material used for testing to the interpretation of results and classification of sequence variants. The article highlights the role of molecular testing in ongoing research and also its role in the diagnostic and therapeutic process in the era of personalized medicine. The spread of genetic testing in high-risk groups, the introduction of more targeted therapies and also the possibility of agnostic therapies could significantly improve the health situation for many women worldwide.

Ovarian Cancer Biomarkers in the COVID-19 Era

Ovarian Cancer (OC) diagnosis is entrusted to CA125 and HE4. Since the latter has been found increased in COVID-19 patients, in this study, we aimed to evaluate the influence of SARS-CoV-2 infection on OC biomarkers. HE4 values above the cut-off were observed in 65% of OC patients and in 48% of SARS-CoV-2-positive patients (not oncologic patients), whereas CA125 values above the cut-off were observed in 71% of OC patients and in 11% of SARS-CoV-2 patients. Hence, by dividing the HE4 levels into quartiles, we can state that altered levels of HE4 in COVID-19 patients were mostly detectable in quartile I (151-300 pmol/L), while altered levels in OC patients were mostly clustered in quartile III (>600, pmol/L). In light of these observations, in order to better discriminate women with ovarian cancer versus those with COVID-19, we established a possible HE4 cut-off of 328 pmol/L by means of a ROC curve. These results demonstrate that the reliability of HE4 as a biomarker in ovarian cancer remains unchanged, despite COVID-19 interference; moreover, it is important for a proper diagnosis that whether the patient has a recent history of SARS-CoV-2 infection is determined.

miRNA-612 suppresses ovarian cancer cell tumorigenicity by downregulating NOB1

MicroRNAs (miRNAs) play crucial roles in cancer progression. Our previous study demonstrated that NIN1/RPN12 binding protein 1 homolog (NOB1) was a functional regulator in the progression of ovarian cancer (OC). However, the role of miRNA-612 (miR-612) in OC has not been elucidated. In this study, we aimed to investigate the regulatory mechanism of NOB1 targeting miRNA, miR-612, in OC tumorigenicity. The miR-612 expression was down-regulated in OC patient tissues and four OC cell lines (Caov3, A2780, SKOV3 and OVCAR3). The miR-612 level was negatively correlated with NOB1 expression, and dual-luciferase reporter assay indicated that miR-612 suppressed NOB1 expression by targeting the 3'UTR of NOB1 transcript. Up-regulation of miR-612 mediated by lentiviral transduction suppressed cell proliferation, colony formation, migration, invasion, and induced apoptosis in OC cell lines. In addition, miR-612 overexpression inhibited tumor growth of OC in vivo by sequestering NOB1 expression. In conclusion, our results suggested that miR-612 directly targeted NOB1 to suppress OC progression. Therefore, the miR-612-NOB1 axis could serve as therapeutic targets for OC.